We consider dual coordinate ascent methods for minimizing a strictly convex (possibly nondifferentiable) function subject to linear constraints. Such methods are useful in large-scale applications (e....
We consider methods for minimizing a convex function f that generate a sequence fx k g by taking x k+1 to be an approximate minimizer of f(x) +D h (x; x k )=c k , where c k ? 0 and D h is the D-functi...
We consider methods for minimizing a convex function f that generate a sequence fx k g by taking x k+1 to be an approximate minimizer of f(x) +D h (x; x k )=c k , where c k ? 0 and D h is the D-functi...
We consider methods for minimizing a convex function f that generate a sequence fx k g by taking x k+1 to be an approximate minimizer of f(x) +D h (x; x k )=c k , where c k ? 0 and D h is the D-functi...
We consider methods for minimizing a convex function f that generate a sequence fx k g by taking x k+1 to be an approximate minimizer of f(x) +D h (x; x k )=c k , where c k ? 0 and D h is the D-functi...
We find the intrinsic hermitean phase operator associated with the NohFoug `eres-Mandel apparatus. This is achieved by first identifying the corresponding phasor basis. Previous, alternative approache...
We find the intrinsic hermitean phase operator associated with the NohFoug `eres-Mandel apparatus. This is achieved by first identifying the corresponding phasor basis. Previous, alternative approache...
Backpropagation based on minimization algorithms is replaced by heuristic search techniques for quantized weights. The resulting algorithm is fast, avoids local minima of the cost function, and may be...